There has been a long history of using machine learning algorithms to tackle the coreference resolution tasks [1]}, most of these systems are either entity-level models [2]}, [3]} or mention-ranking models [4]}, [5]}, [6]} These models are typically trained with heuristic loss functions that assign costs to different error types. Many systems also incorporate entity-level information using joint inference [7]}and systems that build up coreference clusters incrementally[8]}. Most of these models use syntactic parsers for head features and contain hand engineered mention proposal algorithms, and many of them leverages distributed representations of mention pairs or mention clusters to reduce the number of hand-crafted features.
